A branch and bound strategy for Fast Trajectory Similarity Measuring

The increasing use of GPS-enabled devices allowed the collection of huge volumes of movement data in the form of trajectories. An important research problem in trajectory data analysis is the similarity measurement. For most applications, a trajectory-to-trajectory comparison is needed, and therefore, scalability of trajectory similarity measures directly impact the viability to use these techniques. Most similarity measures adopt a dynamic programming implementation, which has a quadratic time complexity in all cases, computing the pair-wise distance for all trajectory points, thus limiting the scalability of these measures. In this article we present a new strategy which takes into account the distance properties in Euclidean spaces to reduce the number of pair-wise point comparison required to determine all the matching points of two trajectories. An extensive experimental evaluation over real GPS trajectory datasets demonstrates the pruning power over 85% in the number of distance computations required to determine the matchings, and a significant execution time speed-up of up to one order of magnitude over the dynamic programming approach.